class_docstr: PQMF module.
  
  This module is based on `Near-perfect-reconstruction pseudo-QMF banks`_.
  
  .. _`Near-perfect-reconstruction pseudo-QMF banks`:
      https://ieeexplore.ieee.org/document/258122
docstr: Analysis with PQMF.
  
  Args:
      x (Tensor): Input tensor (B, 1, T).
  
  Returns:
      Tensor: Output tensor (B, subbands, T // subbands).
docstr_tok: ["Analysis","with","PQMF",".","Args",":","x","(","Tensor",")",":","Input","tensor","(","B",",","1",",","T",")",".","Returns",":","Tensor",":","Output","tensor","(","B",",","subbands",",","T","\/\/","subbands",")","."]
code: def analysis(self, x: torch.Tensor) -> torch.Tensor:
      """Analysis with PQMF.
  
      Args:
          x (Tensor): Input tensor (B, 1, T).
  
      Returns:
          Tensor: Output tensor (B, subbands, T // subbands).
  
      """
      x = F.conv1d(self.pad_fn(x), self.analysis_filter)
      return F.conv1d(x, self.updown_filter, stride=self.subbands)
  
file_path: espnet/espnet2/gan_tts/melgan/pqmf.py
imports: import numpy
  import torch
  import torch.nn.functional
  from scipy.signal import kaiser
label_desc: Use image node_id 2 for calling the PQMF obj's underlying member method code with example usage: obj.analysis(x) and returns: F